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26 - Raising the Ante

Technological Advances in I-O Psychology

from Part VI - Technology in Statistics and Research Methods

Published online by Cambridge University Press:  18 February 2019

Richard N. Landers
Affiliation:
University of Minnesota
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Publisher: Cambridge University Press
Print publication year: 2019

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References

Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine 16 (7). http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory/.Google Scholar
Baker, C. A., Bosco, F. A., Uggerslev, K. L., & Steel, P. (2016). metaBUS: An open search engine of I-O research findings. The Industrial-Organizational Psychologist. 54(1). http://www.siop.org/tip/july16.aspx.Google Scholar
Bhatia, E. N. (2014). Optical character recognition techniques: A review. International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 12191223.Google Scholar
Bono, J. E., Glomb, T. M., Shen, W., Kim, E., & Koch, A. J. (2013). Building positive resources: Effects of positive events and positive reflection on work-stress and health. Academy of Management Journal, 56: 16011627.CrossRefGoogle Scholar
Bosco, F. A., Aguinis, H., Field, J. G., Pierce, C. A., & Dalton, D. R. (2016). HARKing’s threat to organizational research: Evidence from primary and meta-analytic sources. Personnel Psychology, 69, 709750.CrossRefGoogle Scholar
Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015). Correlational effect size benchmarks. Journal of Applied Psychology, 100, 431449.CrossRefGoogle ScholarPubMed
Bosco, F. A., Steel, P., Oswald, F. L., Uggerslev, K. L., & Field, J. G. (2015). Cloud-based meta-analysis to bridge science and practice: Welcome to metaBUS. Personnel Assessment and Decisions, 1, 317.CrossRefGoogle Scholar
Bosco, F. A., Uggerslev, K. L., & Steel, P. (2017). metaBUS as a vehicle for facilitating meta-analysis. Human Resource Management Review, 27, 237254.CrossRefGoogle Scholar
Bosco, F. A., & Uggerslev, K. L. (2018). metaBUS, http://metaBUS.org, September 25.Google Scholar
Boyd, D. & Crawford, K. 2012. Critical questions for big data: provocations for a cultural, technological and scholarly phenomenon, Information, Community, & Society, 15(5), 662679.CrossRefGoogle Scholar
Cheung, M. W.-L. (2015). metaSEM: an R package for meta-analysis using structural equation modeling. Frontiers in Psychology, 5, 1521. doi.org/10.3389/fpsyg.2014.01521.CrossRefGoogle Scholar
Cheung, M. W.-L. (2017). metaSEM: An R package for meta-analysis using structural equation modeling. Modified from the Frontiers in Psychology 2014 manuscript. https://cran.r-project.org/web/packages/metaSEM/vignettes/metaSEM.pdf.Google Scholar
Cortina, J. M., Green, J. P., Keeler, K. R., & Vandenberg, R. J. (2017). Degrees of freedom in SEM: Are we testing the models that we claim to test? Organizational Research Methods, 20(3), 350-378. doi.org/10.1177/1094428116676345.CrossRefGoogle Scholar
Cowls, J. & Schroeder, R. (2015). Causation, correlation, and Big Data in social science research. Policy & Internet, 7, (4), 447472.CrossRefGoogle Scholar
Creswell, J., & Plano Clark, V. (2011). Choosing a mixed method design. In: Creswell, J. & Plano Clark, V. (Eds.), Designing and Conducting Mixed Methods Research (2nd edn., pp. 53105). Thousand Oaks, CA: Sage.Google Scholar
Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2016). Big data and social science: A practical guide to methods and tools. Boca Raton, FL: CRC Press/ Taylor & Francis Group.CrossRefGoogle Scholar
George, G., Osinga, E. C., Lavie, D., & Scott, B. A. (2016). Big data and data science methods for management research, Academy of Management Journal, 59(5), 14931507. doi: 10.5465/amj.2016.4005.CrossRefGoogle Scholar
Holland, S. J., Shore, D. B., & Cortina, J. M. (2016). Review and recommendations for integrated mediation and moderation. Organizational Research Methods, 20(4), 135. http://dx.doi.org/10 .1177/1094428116658958.Google Scholar
Ilies, R., Dimotakis, N., & De Pater, I. (2010). Psychological and physiological reactions to high workloads: Implications for well-being. Personnel Psychology, 63, 407436. doi:10.1111/j.1744–6570.2010.01175.x.CrossRefGoogle Scholar
Kosoff, M. (2015). LinkedIn just bought online learning company Lynda for $1.5 billion, April 9, 2015. www.businessinsider.com/linkedin-buys-lyndacom-for-15-billion-2015–4.Google Scholar
Kozlowski, S. W., Chen, G., & Salas, E. (2017). One hundred years of the Journal of Applied Psychology: Background, evolution, and scientific trends. Journal of Applied Psychology, 102(3), 237.CrossRefGoogle ScholarPubMed
Larsen, K. R., Lee, J., Li, J., & Bong, C. H. (2010). A transdisciplinary approach to construct search and integration, 16th Americas Conference on Information Systems, Lima, Peru, August 1215.Google Scholar
Larsen, K. R. (2017). Inter-Nomological Network. http://inn.colorado.edu, June 14.Google Scholar
Li, J. & Larsen, K. R.. (2011). “Establishing Nomological Networks for Behavioral Science: a Natural Language Processing Based Approach,” International Conference on Information Systems (ICIS), Shanghai, China, December 4th–7th, 2011.Google Scholar
Manning, C. D., Raghavan, P., & Schutze, H. (2009). Introduction to information retrieval. Cambridge, UK: Cambridge University Press.Google Scholar
Mell, P. & Grance, T. (2011). The NIST Definition of Cloud Computing (Technical report). National Institute of Standards and Technology: U.S. Department of Commerce. doi:10.6028/NIST.SP.800–145. Special publication 800–145.CrossRefGoogle Scholar
Mohan, C. (2013). “History Repeats Itself: Sensible and NonsenSQL Aspects of the NoSQL Hoopla”. Proceedings of the 16th International Conference on Extending Database Technology. Retrieved 2017–06-26 from http://openproceedings.org/2013/conf/edbt/Mohan13.pdf.Google Scholar
Najor, M. (2009). Web crawler architecture. In: LIU L., ÖZSU M.T. (Eds) Encyclopedia of Database Systems. Boston, MA: Springer,Google Scholar
Olmedilla, M., Martínez-Torres, M.R., & Toral, S.L. (2016). Harvesting Big Data in social science: A methodological approach for collecting online user-generated content, Computer Standards & Interfaces, 46, 7987. doi.org/10.1016/j.csi.2016.02.003.CrossRefGoogle Scholar
Prajapati, V. (2013). Big data analytics with R and Hadoop. Birmingham, UK: Packt Publishing.Google Scholar
Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world use of Big Data – How innovative enterprises extract value from uncertain data, Executive Report, IBM Institute for Business Value.Google Scholar
Singh, S. (2013). Optical character recognition techniques: a survey. Journal of emerging Trends in Computing and information Sciences, 4(6), 545550.Google Scholar
SINTEF. Big Data, for better or worse: 90 percent of world’s data generated over last two years. ScienceDaily. Retrieved June 20, 2017, from www.sciencedaily.com/releases/2013/05/130522085217.htm.Google Scholar
Sirosh, J. (2015). Microsoft Closes Acquisition of Revolution Analytics. Microsoft. Retrieved November 22, 2015 from blogs.technet.com.Google Scholar
Varian, H. R. (2014). Big data: New tricks for econometrics. The Journal of Economic Perspectives, 28: 327.CrossRefGoogle Scholar
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3):148.CrossRefGoogle Scholar
VoltDB. SQL vs. NoSQL vs. NewSQL. White paper retrieved on 2017–6-26 from www.voltdb.com/wp-content/uploads/2017/05/VoltDB_SQL-vs-NoSQL-vs-NewSQL.pdf.Google Scholar
WIRED. (Jan 19, 2012). Amazon Goes Back to the Future With “NoSQL” Database. Retrieved June 26, 2017, from www.wired.com/2012/01/amazon-dynamodb/.Google Scholar
Xu, Z.W. (2014). Cloud-sea computing systems: Towards thousand-fold improvement in performance per watt for the coming zettabyte era. Journal of Computer science and technology, 29(2),177181. doi: 10.1007/s11390-014–1420-2.CrossRefGoogle Scholar

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